{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/N/project/suicide_study/pnas_replication/results/log/logit_8.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}24 Aug 2020, 00:54:42
{txt}
{com}. 
. if ("`model'" == "logit"){c -(}
.         use "${c -(}home_dir{c )-}/data/processed/suicide_reg_v1_raw.dta", clear
. {c )-}
{txt}
{com}. 
. if ("`model'" == "mi") {c -(}
.         use "${c -(}home_dir{c )-}/data/processed/suicide_reg_v1_imputed_M10.dta", clear  
. {c )-}
{txt}
{com}. 
. * for now, we use the following simple survey weights 
. if ("`model'" == "mi") {c -(}
.         mi svyset `geo_type' [pw=ObsWgt0] 
. {c )-}
{txt}
{com}. else {c -(}
.         svyset `geo_type' [pw=ObsWgt0]  

      {txt}pweight:{col 16}{res}ObsWgt0
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}county
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}
{com}. {c )-}
{txt}
{com}. 
. * margins for each category
. program margin_interact 
{txt}  1{com}.         args X Y k model
{txt}  2{com}.         sum `X', d 
{txt}  3{com}.         local gap = (`r(max)' - `r(min)') / `k' 
{txt}  4{com}.         if ("`model'" == "logit") {c -(}
{txt}  5{com}.                 margin `Y', at(`X' = (`r(min)' (`gap') `r(max)')) predict(pr)
{txt}  6{com}.         {c )-} 
{txt}  7{com}.         else if ("`model'" == "mi") {c -(}
{txt}  8{com}.                 mimrgns `Y', at(`X' = (`r(min)' (`gap') `r(max)')) predict(pr)
{txt}  9{com}.         {c )-}       
{txt} 10{com}. end 
{txt}
{com}. 
. program mchange_mi
{txt}  1{com}.         args X k model
{txt}  2{com}.         if ("`model'" == "logit") {c -(}
{txt}  3{com}. 
.                 if ("`k'" == "continuous") {c -(}
{txt}  4{com}.                         sum `X' if e(sample), d 
{txt}  5{com}.                         margin, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt}  6{com}.                         mlincom  2 - 1, decimal(7) stat(all)    
{txt}  7{com}.                 {c )-} 
{txt}  8{com}.                 else if ("`k'" == "binary") {c -(}
{txt}  9{com}.                         sum `X' if e(sample), d 
{txt} 10{com}.                         margin, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt} 11{com}.                         mlincom  2 - 1, decimal(7) stat(all)
{txt} 12{com}.                 {c )-} 
{txt} 13{com}.                 else if ("`k'" == "categorical") {c -(}
{txt} 14{com}.                         margin `X' if e(sample)==1 , at() pwcompare predict(pr) 
{txt} 15{com}.                 {c )-}
{txt} 16{com}.         {c )-}
{txt} 17{com}. 
.         else if ("`model'" == "mi") {c -(}
{txt} 18{com}. 
.                 if ("`k'" == "continuous") {c -(}
{txt} 19{com}.                         sum `X' , d 
{txt} 20{com}.                         mimrgns, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt} 21{com}.                         mlincom  2 - 1, decimal(7) stat(all)
{txt} 22{com}.                 {c )-} 
{txt} 23{com}.                 else if ("`k'" == "binary") {c -(}
{txt} 24{com}.                         sum `X' , d 
{txt} 25{com}.                         mimrgns, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt} 26{com}.                         mlincom  2 - 1, decimal(7) stat(all)
{txt} 27{com}.                 {c )-} 
{txt} 28{com}.                 else if ("`k'" == "categorical") {c -(}
{txt} 29{com}.                         mimrgns `X' , at()   pwcompare predict(pr)      
{txt} 30{com}.                 {c )-}
{txt} 31{com}.         {c )-}
{txt} 32{com}. end
{txt}
{com}. 
. * create some dummy codings
. tab Race5, gen(race5_nh)

      {txt}Race5 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}  9,804,569       79.53       79.53
{txt}          2 {c |}{res}  1,330,268       10.79       90.32
{txt}          3 {c |}{res}    260,741        2.12       92.44
{txt}          4 {c |}{res}    250,433        2.03       94.47
{txt}          5 {c |}{res}    681,947        5.53      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res} 12,327,958      100.00
{txt}
{com}.         rename race5_nh1 White_nh 
{res}{txt}
{com}.         rename race5_nh2 Black_nh 
{res}{txt}
{com}.         rename race5_nh3 AIAN_nh 
{res}{txt}
{com}.         rename race5_nh4 AsPI_nh 
{res}{txt}
{com}.         rename race5_nh5 Hispanic
{res}{txt}
{com}. 
. tab MarStat5, gen(ms)

   {txt}MarStat5 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}  6,888,231       55.81       55.81
{txt}          2 {c |}{res}    924,747        7.49       63.31
{txt}          3 {c |}{res}  1,267,442       10.27       73.58
{txt}          4 {c |}{res}    237,647        1.93       75.50
{txt}          5 {c |}{res}  3,023,589       24.50      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res} 12,341,656      100.00
{txt}
{com}.         rename ms1 Marrd5
{res}{txt}
{com}.         rename ms2 Widow5
{res}{txt}
{com}.         rename ms3 Divor5
{res}{txt}
{com}.         rename ms4 Separ5
{res}{txt}
{com}.         rename ms5 NvMar5
{res}{txt}
{com}. 
. tab AgeGrp4, gen(ag) 

    {txt}AgeGrp4 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}  1,934,292       15.67       15.67
{txt}          2 {c |}{res}  3,555,999       28.81       44.48
{txt}          3 {c |}{res}  4,309,065       34.91       79.40
{txt}          4 {c |}{res}  2,543,032       20.60      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res} 12,342,388      100.00
{txt}
{com}.         rename ag1 Age_15_24
{res}{txt}
{com}.         rename ag2 Age_25_44
{res}{txt}
{com}.         rename ag3 Age_45_64
{res}{txt}
{com}.         rename ag4 Age_65_Up
{res}{txt}
{com}. 
. destring St, replace 
{txt}St: all characters numeric; {res}replaced {txt}as {res}byte
{txt}
{com}. 
. * set-up equations
. local religion Rat_GC_ProE Rat_GC_ProM Rat_GC_ProB Rat_GC_Cath Rat_GC_Jew Rat_GC_Oth
{txt}
{com}. local contextual_control Rat_Poverty Rat_Mig_Cum Pop_Den
{txt}
{com}. 
. local religion Rat_GC_ProE Rat_GC_ProM Rat_GC_ProB Rat_GC_Jew Rat_GC_Oth
{txt}
{com}. local contextual_control Rat_Poverty Rat_Mig_Cum Pop_Den
{txt}
{com}. 
. local demographics_raw i.Female c.RAT_Female i.AgeGrp4 c.RAT_AgeGrp4_2 c.RAT_AgeGrp4_3 c.RAT_AgeGrp4_4 i.Race5 c.RAT_Race5_2 c.RAT_Race5_3 c.RAT_Race5_4 c.RAT_Race5_5 i.BornUSA c.RAT_BornUSA i.MarStat5 c.RAT_MarStat5_2 c.RAT_MarStat5_3 c.RAT_MarStat5_4 c.RAT_MarStat5_5
{txt}
{com}. local demographics_individual i.Female i.AgeGrp4 i.Race5 i.BornUSA i.MarStat5 
{txt}
{com}. local demographics_county c.RAT_Female c.RAT_AgeGrp4_2 c.RAT_AgeGrp4_3 c.RAT_AgeGrp4_4  c.RAT_Race5_2 c.RAT_Race5_3 c.RAT_Race5_4 c.RAT_Race5_5 c.RAT_BornUSA c.RAT_MarStat5_2 c.RAT_MarStat5_3 c.RAT_MarStat5_4 c.RAT_MarStat5_5
{txt}
{com}. local demographics_same i.Female c.std_same_prop_Sex i.AgeGrp4 c.std_same_prop_AgeGrp4 i.Race5 c.std_same_prop_Race5 i.BornUSA c.std_same_prop_BornUSA i.MarStat5 c.std_same_prop_MarStat5 
{txt}
{com}. local demographics_inter i.Female##c.std_same_prop_Sex i.AgeGrp4##c.std_same_prop_AgeGrp4 i.Race5##c.std_same_prop_Race5 i.BornUSA##c.std_same_prop_BornUSA i.MarStat5##c.std_same_prop_MarStat5
{txt}
{com}. 
. 
. if (`model_version' == 1){c -(}
.         local model_eq i.Year `demographics_raw' `contextual_control' `religion' 
.         local margin_demographics Female RAT_Female AgeGrp4 RAT_AgeGrp4_2 RAT_AgeGrp4_3 RAT_AgeGrp4_4 Race5 RAT_Race5_2 RAT_Race5_3 RAT_Race5_4 RAT_Race5_5 BornUSA RAT_BornUSA MarStat5 RAT_MarStat5_2 RAT_MarStat5_3 RAT_MarStat5_4 RAT_MarStat5_5 
. {c )-}
{txt}
{com}. if (`model_version' == 2){c -(}
.         local model_eq i.Year `demographics_raw' `contextual_control' `religion' i.UnEmpl c.RAT_UnEmpl i.PhysProb c.RAT_PhysProb
.         local margin_demographics Female RAT_Female AgeGrp4 RAT_AgeGrp4_2 RAT_AgeGrp4_3 RAT_AgeGrp4_4 Race5 RAT_Race5_2 RAT_Race5_3 RAT_Race5_4 RAT_Race5_5 BornUSA RAT_BornUSA MarStat5 RAT_MarStat5_2 RAT_MarStat5_3 RAT_MarStat5_4 RAT_MarStat5_5 UnEmpl RAT_UnEmpl PhysProb RAT_PhysProb
. {c )-}
{txt}
{com}. if (`model_version' == 3){c -(}
.         local model_eq i.Year `demographics_same' `contextual_control' `religion' 
.         local margin_demographics Female std_same_prop_Sex AgeGrp4 std_same_prop_AgeGrp4 Race5 std_same_prop_Race5 BornUSA std_same_prop_BornUSA MarStat5 std_same_prop_MarStat5 
. {c )-}
{txt}
{com}. if (`model_version' == 4){c -(}
.         local model_eq i.Year `demographics_same' `contextual_control' `religion' i.UnEmpl c.std_same_prop_UnEmpl i.PhysProb c.std_same_prop_PhysProb
.         local margin_demographics Female std_same_prop_Sex AgeGrp4 std_same_prop_AgeGrp4 Race5 std_same_prop_Race5 BornUSA std_same_prop_BornUSA MarStat5 std_same_prop_MarStat5 UnEmpl std_same_prop_UnEmpl PhysProb std_same_prop_PhysProb
. {c )-}
{txt}
{com}. if (`model_version' == 5){c -(}
.         local model_eq i.Year `demographics_inter' `contextual_control' `religion' 
. {c )-}
{txt}
{com}. if (`model_version' == 6){c -(}
.         local model_eq i.Year `demographics_inter' `contextual_control' `religion' i.UnEmpl##c.std_same_prop_UnEmpl i.PhysProb##c.std_same_prop_PhysProb
. {c )-}       
{txt}
{com}. 
. if (`model_version' == 7){c -(}
.         local model_eq i.Year `demographics_individual' `contextual_control' `religion' 
.         local margin_demographics Female AgeGrp4 Race5 BornUSA MarStat5 
. {c )-}
{txt}
{com}. if (`model_version' == 8){c -(}
.         local model_eq i.Year `demographics_county' `contextual_control' `religion' 
.         local margin_demographics RAT_Female RAT_AgeGrp4_2 RAT_AgeGrp4_3 RAT_AgeGrp4_4 RAT_Race5_2 RAT_Race5_3 RAT_Race5_4 RAT_Race5_5 RAT_BornUSA RAT_MarStat5_2 RAT_MarStat5_3 RAT_MarStat5_4 RAT_MarStat5_5 
. {c )-}
{txt}
{com}. 
. if (`model_version' == 9){c -(}
.         local model_eq i.Year `demographics_individual' `contextual_control' `religion'  i.UnEmpl i.PhysProb
.         local margin_demographics Female AgeGrp4 Race5 BornUSA MarStat5 UnEmpl PhysProb
. {c )-}
{txt}
{com}. if (`model_version' == 10){c -(}
.         local model_eq i.Year `demographics_county' `contextual_control' `religion'  c.RAT_UnEmpl c.RAT_PhysProb
.         local margin_demographics RAT_Female RAT_AgeGrp4_2 RAT_AgeGrp4_3 RAT_AgeGrp4_4 RAT_Race5_2 RAT_Race5_3 RAT_Race5_4 RAT_Race5_5 RAT_BornUSA RAT_MarStat5_2 RAT_MarStat5_3 RAT_MarStat5_4 RAT_MarStat5_5 RAT_UnEmpl RAT_PhysProb
. {c )-}
{txt}
{com}. 
. * test how long it would take.
. * mi estimate: svy: mean Suic 
. * local demographics i.Female c.RAT_Female i.AgeGrp4 c.RAT_AgeGrp4_2 c.RAT_AgeGrp4_3 c.RAT_AgeGrp4_4 i.Race5 c.RAT_Race5_2 c.RAT_Race5_3 c.RAT_Race5_4 c.RAT_Race5_5 i.BornUSA c.RAT_BornUSA i.MarStat5 c.RAT_MarStat5_2 c.RAT_MarStat5_3 c.RAT_MarStat5_4 c.RAT_MarStat5_5
. * mi estimate: svy: logit Suic i.St `demographics' UnEmpl RAT_UnEmpl PhysProb RAT_PhysProb
. 
. * main effects : margins
. if ("`model'" == "mi"){c -(}
. 
.         mi estimate: svy: logit Suic i.St `model_eq', or 
.         estimates store m1 
. 
.         if (`model_version' <= 4 | `model_version' >= 9){c -(}
.                 if (`model_version' != 10){c -(}
.                         estimates restore m1
.                         mchange_mi Female "binary" "mi"
.                         estimates restore m1
.                         mchange_mi AgeGrp4 "categorical" "mi"
.                         estimates restore m1
.                         mchange_mi Race5 "categorical" "mi"
.                         estimates restore m1
.                         mchange_mi BornUSA "binary" "mi"
.                         estimates restore m1
.                         mchange_mi MarStat5 "categorical" "mi"
.                 {c )-}
.                 
.                 if (`model_version' == 1 | `model_version' == 2 | `model_version' == 10) {c -(}
.                         estimates restore m1
.                         mchange_mi RAT_Female "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_AgeGrp4_2 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_AgeGrp4_3 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_AgeGrp4_4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_2 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_3 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_5 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_BornUSA "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_2 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_3 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_5 "continuous" "mi"
.                 {c )-}
.                 if (`model_version' == 3 | `model_version' == 4) {c -(}
.                         estimates restore m1
.                         mchange_mi std_same_prop_Sex "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_AgeGrp4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_Race5 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_BornUSA "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_MarStat5 "continuous" "mi"
.                 {c )-}
.         
.                 if (`model_version' == 2 | `model_version' == 4 | `model_version' == 9 ){c -(}
.                         estimates restore m1
.                         mchange_mi UnEmpl "categorical" "mi"
.                         estimates restore m1
.                         mchange_mi PhysProb "categorical" "mi"
.                 {c )-}
.         
.                 if (`model_version' == 2 | `model_version' == 10){c -(}
.                         estimates restore m1
.                         mchange_mi RAT_UnEmpl "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_PhysProb "continuous" "mi"
.                 {c )-}
.                 if (`model_version' == 4){c -(}
.                         estimates restore m1
.                         mchange_mi std_same_prop_UnEmpl "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_PhysProb "continuous" "mi"
.                 {c )-}               
.         {c )-}
. {c )-}
{txt}
{com}. 
. if ("`model'" == "logit"){c -(}
.         svy: logit Suic i.St `model_eq', or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 47}Number of obs{col 65}= {res}  11,830,401
{txt}{col 1}Number of PSUs{col 20}= {res}      918{txt}{col 47}Population size{col 65}={res}  419,698,483
{txt}{col 47}Design df{col 65}= {res}         917
{txt}{col 47}F({res}  42{txt},{res}    876{txt}){col 65}= {res}       99.71
{txt}{col 47}Prob > F{col 65}= {res}      0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        Suic{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}St {c |}
{space 10}8  {c |}{col 14}{res}{space 2} .9288229{col 26}{space 2} .0891537{col 37}{space 1}   -0.77{col 46}{space 3}0.442{col 54}{space 4} .7693461{col 67}{space 3} 1.121358
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .7366245{col 26}{space 2} .0758671{col 37}{space 1}   -2.97{col 46}{space 3}0.003{col 54}{space 4} .6018143{col 67}{space 3} .9016329
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .6343312{col 26}{space 2} .0626302{col 37}{space 1}   -4.61{col 46}{space 3}0.000{col 54}{space 4} .5225915{col 67}{space 3}  .769963
{txt}{space 9}24  {c |}{col 14}{res}{space 2}  .618026{col 26}{space 2} .0633619{col 37}{space 1}   -4.69{col 46}{space 3}0.000{col 54}{space 4} .5053866{col 67}{space 3} .7557703
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .4984004{col 26}{space 2} .0508127{col 37}{space 1}   -6.83{col 46}{space 3}0.000{col 54}{space 4} .4080208{col 67}{space 3} .6087997
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .5576287{col 26}{space 2} .0610692{col 37}{space 1}   -5.33{col 46}{space 3}0.000{col 54}{space 4} .4497817{col 67}{space 3} .6913349
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .8511699{col 26}{space 2} .1242617{col 37}{space 1}   -1.10{col 46}{space 3}0.270{col 54}{space 4} .6391249{col 67}{space 3} 1.133566
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .7342543{col 26}{space 2} .0765895{col 37}{space 1}   -2.96{col 46}{space 3}0.003{col 54}{space 4} .5983303{col 67}{space 3} .9010564
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .7116897{col 26}{space 2} .0751163{col 37}{space 1}   -3.22{col 46}{space 3}0.001{col 54}{space 4} .5785364{col 67}{space 3} .8754889
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .7856425{col 26}{space 2} .0722656{col 37}{space 1}   -2.62{col 46}{space 3}0.009{col 54}{space 4} .6558818{col 67}{space 3} .9410754
{txt}{space 9}44  {c |}{col 14}{res}{space 2}  .552882{col 26}{space 2} .0559312{col 37}{space 1}   -5.86{col 46}{space 3}0.000{col 54}{space 4} .4533238{col 67}{space 3}  .674305
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .7588282{col 26}{space 2} .0791413{col 37}{space 1}   -2.65{col 46}{space 3}0.008{col 54}{space 4} .6183734{col 67}{space 3} .9311853
{txt}{space 9}49  {c |}{col 14}{res}{space 2} 1.550162{col 26}{space 2} .2647174{col 37}{space 1}    2.57{col 46}{space 3}0.010{col 54}{space 4} 1.108734{col 67}{space 3} 2.167339
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .7978718{col 26}{space 2} .0793913{col 37}{space 1}   -2.27{col 46}{space 3}0.023{col 54}{space 4} .6563317{col 67}{space 3} .9699354
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .6380573{col 26}{space 2} .0637862{col 37}{space 1}   -4.49{col 46}{space 3}0.000{col 54}{space 4} .5243884{col 67}{space 3} .7763656
{txt}{space 12} {c |}
{space 8}Year {c |}
{space 7}2006  {c |}{col 14}{res}{space 2} .9846072{col 26}{space 2} .0161902{col 37}{space 1}   -0.94{col 46}{space 3}0.346{col 54}{space 4} .9533402{col 67}{space 3}   1.0169
{txt}{space 7}2007  {c |}{col 14}{res}{space 2} 1.002337{col 26}{space 2} .0154766{col 37}{space 1}    0.15{col 46}{space 3}0.880{col 54}{space 4} .9724185{col 67}{space 3} 1.033175
{txt}{space 7}2008  {c |}{col 14}{res}{space 2} 1.011788{col 26}{space 2} .0159047{col 37}{space 1}    0.75{col 46}{space 3}0.456{col 54}{space 4} .9810503{col 67}{space 3} 1.043488
{txt}{space 7}2009  {c |}{col 14}{res}{space 2} 1.043211{col 26}{space 2}  .016769{col 37}{space 1}    2.63{col 46}{space 3}0.009{col 54}{space 4} 1.010815{col 67}{space 3} 1.076646
{txt}{space 7}2010  {c |}{col 14}{res}{space 2} 1.046923{col 26}{space 2} .0175086{col 37}{space 1}    2.74{col 46}{space 3}0.006{col 54}{space 4} 1.013119{col 67}{space 3} 1.081854
{txt}{space 7}2011  {c |}{col 14}{res}{space 2} 1.085365{col 26}{space 2}  .018468{col 37}{space 1}    4.81{col 46}{space 3}0.000{col 54}{space 4} 1.049719{col 67}{space 3} 1.122222
{txt}{space 12} {c |}
{space 2}RAT_Female {c |}{col 14}{res}{space 2} .9720636{col 26}{space 2} .0078761{col 37}{space 1}   -3.50{col 46}{space 3}0.000{col 54}{space 4} .9567286{col 67}{space 3} .9876444
{txt}RAT_AgeGrp~2 {c |}{col 14}{res}{space 2} .9963311{col 26}{space 2} .0041673{col 37}{space 1}   -0.88{col 46}{space 3}0.380{col 54}{space 4} .9881861{col 67}{space 3} 1.004543
{txt}RAT_AgeGrp~3 {c |}{col 14}{res}{space 2} .9938464{col 26}{space 2} .0043318{col 37}{space 1}   -1.42{col 46}{space 3}0.157{col 54}{space 4} .9853812{col 67}{space 3} 1.002384
{txt}RAT_AgeGrp~4 {c |}{col 14}{res}{space 2} 1.020462{col 26}{space 2} .0068461{col 37}{space 1}    3.02{col 46}{space 3}0.003{col 54}{space 4} 1.007114{col 67}{space 3} 1.033986
{txt}{space 1}RAT_Race5_2 {c |}{col 14}{res}{space 2} .9950336{col 26}{space 2}  .001244{col 37}{space 1}   -3.98{col 46}{space 3}0.000{col 54}{space 4} .9925952{col 67}{space 3} .9974779
{txt}{space 1}RAT_Race5_3 {c |}{col 14}{res}{space 2} 1.009455{col 26}{space 2} .0035647{col 37}{space 1}    2.66{col 46}{space 3}0.008{col 54}{space 4} 1.002483{col 67}{space 3} 1.016475
{txt}{space 1}RAT_Race5_4 {c |}{col 14}{res}{space 2} 1.004704{col 26}{space 2} .0052698{col 37}{space 1}    0.89{col 46}{space 3}0.371{col 54}{space 4} .9944148{col 67}{space 3}   1.0151
{txt}{space 1}RAT_Race5_5 {c |}{col 14}{res}{space 2} 1.000771{col 26}{space 2} .0032525{col 37}{space 1}    0.24{col 46}{space 3}0.812{col 54}{space 4} .9944085{col 67}{space 3} 1.007175
{txt}{space 1}RAT_BornUSA {c |}{col 14}{res}{space 2} 1.010659{col 26}{space 2} .0037194{col 37}{space 1}    2.88{col 46}{space 3}0.004{col 54}{space 4} 1.003386{col 67}{space 3} 1.017985
{txt}RAT_MarSta~2 {c |}{col 14}{res}{space 2} .9710241{col 26}{space 2} .0153215{col 37}{space 1}   -1.86{col 46}{space 3}0.063{col 54}{space 4} .9414157{col 67}{space 3} 1.001564
{txt}RAT_MarSta~3 {c |}{col 14}{res}{space 2} 1.055049{col 26}{space 2} .0076486{col 37}{space 1}    7.39{col 46}{space 3}0.000{col 54}{space 4} 1.040144{col 67}{space 3} 1.070167
{txt}RAT_MarSta~4 {c |}{col 14}{res}{space 2} 1.025711{col 26}{space 2} .0207257{col 37}{space 1}    1.26{col 46}{space 3}0.209{col 54}{space 4} .9858314{col 67}{space 3} 1.067203
{txt}RAT_MarSta~5 {c |}{col 14}{res}{space 2} .9997237{col 26}{space 2} .0031045{col 37}{space 1}   -0.09{col 46}{space 3}0.929{col 54}{space 4} .9936495{col 67}{space 3} 1.005835
{txt}{space 1}Rat_Poverty {c |}{col 14}{res}{space 2} .9990338{col 26}{space 2}   .00229{col 37}{space 1}   -0.42{col 46}{space 3}0.673{col 54}{space 4} .9945497{col 67}{space 3} 1.003538
{txt}{space 1}Rat_Mig_Cum {c |}{col 14}{res}{space 2} .1717378{col 26}{space 2} .0822027{col 37}{space 1}   -3.68{col 46}{space 3}0.000{col 54}{space 4} .0671271{col 67}{space 3} .4393735
{txt}{space 5}Pop_Den {c |}{col 14}{res}{space 2} 1.000005{col 26}{space 2} 5.81e-06{col 37}{space 1}    0.89{col 46}{space 3}0.374{col 54}{space 4} .9999938{col 67}{space 3} 1.000017
{txt}{space 1}Rat_GC_ProE {c |}{col 14}{res}{space 2} 1.000008{col 26}{space 2} 8.81e-06{col 37}{space 1}    0.96{col 46}{space 3}0.337{col 54}{space 4} .9999912{col 67}{space 3} 1.000026
{txt}{space 1}Rat_GC_ProM {c |}{col 14}{res}{space 2} 1.000006{col 26}{space 2} .0000111{col 37}{space 1}    0.52{col 46}{space 3}0.602{col 54}{space 4} .9999841{col 67}{space 3} 1.000027
{txt}{space 1}Rat_GC_ProB {c |}{col 14}{res}{space 2} .9999537{col 26}{space 2} .0000424{col 37}{space 1}   -1.09{col 46}{space 3}0.276{col 54}{space 4} .9998705{col 67}{space 3} 1.000037
{txt}{space 2}Rat_GC_Jew {c |}{col 14}{res}{space 2} 1.000082{col 26}{space 2}  .000073{col 37}{space 1}    1.12{col 46}{space 3}0.262{col 54}{space 4} .9999387{col 67}{space 3} 1.000225
{txt}{space 2}Rat_GC_Oth {c |}{col 14}{res}{space 2} .9999282{col 26}{space 2} .0000247{col 37}{space 1}   -2.91{col 46}{space 3}0.004{col 54}{space 4} .9998798{col 67}{space 3} .9999766
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0002756{col 26}{space 2} .0001978{col 37}{space 1}  -11.42{col 46}{space 3}0.000{col 54}{space 4} .0000674{col 67}{space 3} .0011275
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}
{com}.         estimates store m1 
. 
.         if (`model_version' <= 4 | `model_version' == 7 | `model_version' == 8){c -(}
.                 mchange `margin_demographics', amount(all) delta(100) statistics(all) decimals(7)

{res}svy logit: Changes in Pr(y) | Number of obs = 11780355

{txt}Expression: Pr(Suic), predict(pr)
{res}
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{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{res:{lalign 13:RAT Female}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000184}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1517944}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000435}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000068}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.43e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000043}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0004045}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000067}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000019}}}{space 1}{space 1}{ralign 9:{res:{sf:-3.55e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0001448}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001592}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001304}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.97e+01}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000437}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0008072}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000692}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000182}}}{space 1}{space 1}{ralign 9:{res:{sf:-3.36e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000044}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0004874}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000068}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000019}}}{space 1}{space 1}{ralign 9:{res:{sf:-3.50e+00}}}{space 1}
{space 0}{res:{lalign 13:RAT AgeGrp~2}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000006}}}{space 1}{space 1}{ralign 9:{res:{sf:0.4337636}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000022}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000010}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.7831119}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000006}}}{space 1}{space 1}{ralign 9:{res:{sf:0.3787641}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000018}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000007}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.8806019}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000473}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2883932}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001347}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000401}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.06e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000143}}}{space 1}{space 1}{ralign 9:{res:{sf:0.3761328}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000460}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000174}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.8854746}}}{space 1}
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{space 0}{res:{lalign 13:RAT AgeGrp~3}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
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{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000009}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1562401}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000023}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.42e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000708}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0507909}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001419}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000002}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.96e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000227}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1571353}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000542}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000088}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.42e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000009}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1575174}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000023}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.41e+00}}}{space 1}
{space 0}{res:{lalign 13:RAT AgeGrp~4}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
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{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000031}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0028661}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000011}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000052}}}{space 1}{space 1}{ralign 9:{res:{sf:2.9898055}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0010108}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1954320}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0005203}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0025420}}}{space 1}{space 1}{ralign 9:{res:{sf:1.2956212}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000627}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0045047}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000195}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001059}}}{space 1}{space 1}{ralign 9:{res:{sf:2.8475121}}}{space 1}
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{space 0}{res:{lalign 13:RAT Race5 2}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000008}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001885}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:-3.75e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000008}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000655}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000011}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:-4.01e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000603}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000003}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000831}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000375}}}{space 1}{space 1}{ralign 9:{res:{sf:-5.19e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000450}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000140}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000652}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000248}}}{space 1}{space 1}{ralign 9:{res:{sf:-4.37e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000008}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000683}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000011}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:-4.00e+00}}}{space 1}
{space 0}{res:{lalign 13:RAT Race5 3}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000014}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0074906}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000025}}}{space 1}{space 1}{ralign 9:{res:{sf:2.6801654}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000015}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0082740}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000025}}}{space 1}{space 1}{ralign 9:{res:{sf:2.6464315}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0002402}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0849041}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000331}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0005136}}}{space 1}{space 1}{ralign 9:{res:{sf:1.7247798}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001208}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0423302}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000042}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002375}}}{space 1}{space 1}{ralign 9:{res:{sf:2.0330976}}}{space 1}
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{space 0}{res:{lalign 13:RAT Race5 4}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000007}}}{space 1}{space 1}{ralign 9:{res:{sf:0.3658483}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000008}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000023}}}{space 1}{space 1}{ralign 9:{res:{sf:0.9047258}}}{space 1}
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{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000921}}}{space 1}{space 1}{ralign 9:{res:{sf:0.4754662}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001611}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0003453}}}{space 1}{space 1}{ralign 9:{res:{sf:0.7139073}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000141}}}{space 1}{space 1}{ralign 9:{res:{sf:0.3863787}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000179}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000461}}}{space 1}{space 1}{ralign 9:{res:{sf:0.8666167}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000007}}}{space 1}{space 1}{ralign 9:{res:{sf:0.3713964}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000009}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000023}}}{space 1}{space 1}{ralign 9:{res:{sf:0.8942993}}}{space 1}
{space 0}{res:{lalign 13:RAT Race5 5}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000001}}}{space 1}{space 1}{ralign 9:{res:{sf:0.8113750}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000009}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000011}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2387213}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000001}}}{space 1}{space 1}{ralign 9:{res:{sf:0.8125911}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000009}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000011}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2371530}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000123}}}{space 1}{space 1}{ralign 9:{res:{sf:0.8194393}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000936}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001183}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2283317}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000081}}}{space 1}{space 1}{ralign 9:{res:{sf:0.8161097}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000606}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000768}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2326183}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000001}}}{space 1}{space 1}{ralign 9:{res:{sf:0.8125202}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000009}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000011}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2372444}}}{space 1}
{space 0}{res:{lalign 13:RAT BornUSA}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000006}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000006}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000007}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.81e+01}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000016}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0043153}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000028}}}{space 1}{space 1}{ralign 9:{res:{sf:2.8612786}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0002901}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0762293}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000307}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0006109}}}{space 1}{space 1}{ralign 9:{res:{sf:1.7750016}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000721}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0010382}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000291}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001151}}}{space 1}{space 1}{ralign 9:{res:{sf:3.2905081}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000016}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0041151}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000027}}}{space 1}{space 1}{ralign 9:{res:{sf:2.8764421}}}{space 1}
{space 0}{res:{lalign 13:RAT MarSta~2}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
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{res}
{txt}{p 0 0 2}Average predictions{p_end}

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:0}{space 1}{space 1}{ralign 9:1}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:Pr(y|base)}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.9998462}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001538}}}{space 1}

{col 1}1: Delta equals 100.
{com}.         {c )-}
. 
. {c )-}
{txt}
{com}. 
. if (`model_version' == 5 | `model_version' == 6) {c -(}
.         estimates restore m1
.         margin_interact std_same_prop_Sex Female 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_AgeGrp4 AgeGrp4 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_Race5 Race5 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_BornUSA BornUSA 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_MarStat5 MarStat5 5 `model'
. 
.         if (`model_version' == 6) {c -(}
.                 estimates restore m1
.                 margin_interact std_same_prop_UnEmpl UnEmpl 5 `model'
.                 estimates restore m1
.                 margin_interact std_same_prop_PhysProb PhysProb 5 `model'
.         {c )-}
. 
. {c )-}
{txt}
{com}. 
. 
. log close 
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/N/project/suicide_study/pnas_replication/results/log/logit_8.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}24 Aug 2020, 02:36:01
{txt}{.-}
{smcl}
{txt}{sf}{ul off}